Machine learning and DFT investigation of CO, CO<sub>2</sub> and CH<sub>4</sub> adsorption on pristine and defective two-dimensional magnesene-Reference-Cited by-同舟云学术

Machine learning and DFT investigation of CO, CO₂ and CH₄ adsorption on pristine and defective two-dimensional magnesene

Published:2023 Issue:18 Volume:25 Page:13170-13182
ISSN:1463-9076
Container-title:Physical Chemistry Chemical Physics
language:en
Short-container-title:Phys. Chem. Chem. Phys.

Author:

Thomas Siby¹^ORCID,Mayr Felix¹^ORCID,Kulangara Madam Ajith²^ORCID,Gagliardi Alessio¹^ORCID

Affiliation:

1. School of Computation, Information and Technology (SoCIT), Technical University of Munich (TUM), Hans-Piloty-Strasse 1, 85748 Garching, Munich, Germany

2. Department of Physics, National Institute of Technology Karnataka (NITK), Surathkal, PO: Srinivasnagar-575025, Mangalore, Karnataka, India

Abstract

Unlocking the secrets of gas adsorption on pristine and defective magnesene (2D-Mg), a 2D material: Insights revealed through DFT and ML techniques with CO, CO2, and CH4 gases.

Funder

Deutsche Forschungsgemeinschaft

Publisher

Royal Society of Chemistry (RSC)

Subject

Physical and Theoretical Chemistry,General Physics and Astronomy

Link

http://pubs.rsc.org/en/content/articlepdf/2023/CP/D3CP00613A

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2. B3S monolayer: prediction of a high-performance anode material for lithium-ion batteries

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